linking phenotype changes to internal/external longitudinal time series in a single human
TRANSCRIPT
“Linking Phenotype Changes to Internal/External Longitudinal Time Series in a Single Human”
Invited Presentation at EMBC ‘16 38th International Conference of the IEEE Engineering in Medicine and Biology Society
Symposium: The Quantified Self: Visions for the Next Decade of Persistent Physiological MonitoringOrlando, FL
August 18, 2016
Dr. Larry SmarrDirector, California Institute for Telecommunications and Information Technology
Harry E. Gruber Professor, Dept. of Computer Science and Engineering
Jacobs School of Engineering, UCSDhttp://lsmarr.calit2.net
1
Abstract
Taking the point of view that the human body is a dynamical coupled system, I have been involved in an experiment for most of the last decade to gather time series data on key body variables. By taking blood and stool samples on a regular basis (bimonthly to quarterly), I have developed a detailed longitudinal time series of ~200 biomakers as well as the microbiome ecology. To define phenotype changes, I have daily weight and symptom data, as well as wireless sensors. Since I have colonic Crohn’s autoimmune disease, one sees episodic variation in these variables with excursions of 10x to 100x above healthy values, demonstrating that single values of these variables randomly taken in time (i.e. traditional medical care) is nearly meaningless. By following the dynamics of my gut microbiome ecology, we have discovered an abrupt shift in the microbiome ecology that is strongly coupled to changes in prescription medicines and external variables such as weight and autoimmune symptoms. This experiment provides a window into the future of personalized precision medicine.
Over the Last Decade, I Have Used a Variety of Personal SensorsTo Quantify My Body & Drive Behavioral Change
Withings/iPhone-Blood Pressure
Zeo-SleepAzumio-Heart Rate
MyFitnessPal-Calories Ingested
FitBit -Daily Steps &
Calories Burned
Withings WiFi Scale -Daily Weight
Wireless Monitoring Produced Time Series That Helped Me Improve My Health
Since Starting November 3, 2011Total Distance Tracked 6180 miles = Round Trip San Diego to Nome, Alaska
Total Vertical Distance Climbed 190,000 ft. = 6.5x Mt. Everest
My Resting Heartrate Fell from 70 to 40!
Elliptical
Walking
Sunday January 17, 2016137
42
I Increased Walking,
Aerobic, and Resistance Training,
All of WhichHave Health
Benefits
From Measuring Macro-Variables to Measuring Your Internal Variables
www.technologyreview.com/biomedicine/39636
As a Model for the Precision Medicine Initiative, I Have Tracked My Internal Biomarkers To Understand My Body’s Dynamics
My Quarterly Blood DrawCalit2 64 Megapixel VROOM
Only One of My Blood Measurements Was Far Out of Range--Indicating Chronic Inflammation
Normal Range <1 mg/L
27x Upper Limit
Complex Reactive Protein (CRP) is a Blood Biomarker for Detecting Presence of Inflammation
Episodic Peaks in Inflammation Followed by Spontaneous Drops
Adding Stool Tests RevealedOscillatory Behavior in an Immune Variable Which is Antibacterial
Normal Range<7.3 µg/mL
124x Upper Limit for Healthy
Lactoferrin is a Protein Shed from Neutrophils -An Antibacterial that Sequesters Iron
TypicalLactoferrin Value
for Active
Inflammatory Bowel Disease
(IBD)
Descending Colon
Sigmoid ColonThreading Iliac Arteries
Major Kink
Confirming the IBD (Colonic Crohn’s) Hypothesis:Finding the “Smoking Gun” with MRI Imaging
I Obtained the MRI Slices From UCSD Medical Services
and Converted to Interactive 3D Working With Calit2 Staff
Transverse ColonLiver
Small Intestine
Diseased Sigmoid ColonCross SectionMRI Jan 2012
Severe ColonWall Swelling
Time Series Reveals Oscillations in Immune BiomarkersAssociated with Time Progression of Autoimmune Disease
Immune &Inflammation
Variables
Weekly Symptoms
PharmaTherapies
StoolSamples
2009 20142013201220112010 2015
What Can We Learn From the Gut Microbiome Time Series In an Individual?
Your Microbiome is Your “Near-Body” Environment
and its CellsContain 100x as Many DNA GenesAs Your Human DNA-Bearing Cells
To Understand the Autoimmune Dynamics of the Immune System
We Must Consider the Human Microbiome
Inclusion of the “Dark Matter” of the BodyWill Radically Alter Medicine
Evolving Microbiome Environmental Pressures: Dynamical Innate and Adaptive Immune Oscillations in Colon
Normal <600
Innate Immune System
Normal 50 to 200
Adaptive Immune SystemThese Must Be Coupled to
A Dynamic Microbiome Ecology
We are Genomically Analyzing My Stool Time Series in a Collaboration with the UCSD Knight Lab
Larry’s 40 Stool Samples Over 3.5 Years to Rob’s lab on April 30, 2015
LS Weekly Weight During Period of 16S Microbiome AnalysisAbrupt Change in Weight and in Symptoms at January 1, 2014
Lialda
Uceris
Frequent IBD SymptomsWeight Loss
Few IBD SymptomsWeight Gain
Source: Larry Smarr, UCSD
My Microbiome Ecology Time Series Over 3 Years
Source Justine Debelius, Knight Lab, UC San Diego
Coloring Samples Before (Blue) and After (Red) January 2014Reveals Clustering
Source Justine Debelius, Knight Lab, UC San Diego
An Apparent Sudden Phase Change Occurs
Source Justine Debelius, Knight Lab, UC San Diego
My Gut Microbiome Ecology Shifted After Drug Therapy Between Two Time-Stable Equilibriums Correlated to Physical Symptoms
Lialda &
Uceris
12/1/13 to
1/1/14
12/1/13-1/1/14
Frequent IBD SymptomsWeight Loss
7/1/12 to 12/1/14
Blue Balls on Diagram to the Right
Principal Coordinate Analysis of Microbiome Ecology
PCoA by Justine Debelius and Jose Navas, Knight Lab, UCSD
Weight Data from Larry Smarr, Calit2, UCSD
Weekly Weight
Few IBD SymptomsWeight Gain 1/1/14 to 8/1/15
Red Balls on Diagram to the Right
To Expand IBD Project the Knight/Smarr Labs Were Awarded ~ 1 CPU-Century Supercomputing Time
• Smarr Gut Microbiome Time Series– From 7 Samples Over 1.5 Years – To 50 Samples Over 4 Years
• IBD Patients: From 5 Crohn’s Disease and 2 Ulcerative Colitis Patients to ~100 Patients– 50 Carefully Phenotyped Patients Drawn from Sandborn BioBank– 43 Metagenomes from the RISK Cohort of Newly Diagnosed IBD patients
• New Software Suite from Knight Lab– Re-annotation of Reference Genomes, Functional / Taxonomic Variations– Novel Compute-Intensive Assembly Algorithms from Pavel Pevzner
8x Compute Resources Over Prior Study
What I Have Measured Is Rapidly Being Supersededto Include Deep Characterization of the Human Body
The Future Foundation of Medicine is an Exponential Scaling-Up of the Number of Deeply Quantified Humans
Source: @EricTopolTwitter 9/27/2014
Thanks to Our Great Team!
Calit2@UCSD Future Patient TeamJerry SheehanTom DeFanti Joe Keefe John GrahamKevin PatrickMehrdad YazdaniJurgen Schulze Andrew Prudhomme Philip Weber Fred RaabErnesto Ramirez
JCVI TeamKaren Nelson Shibu Yooseph Manolito Torralba
AyasdiDevi RamananPek Lum
UCSD Metagenomics TeamWeizhong Li Sitao Wu
SDSC TeamMichael Norman Mahidhar Tatineni Robert Sinkovits
UCSD Health Sciences TeamDavid BrennerRob Knight Lab Justine Debelius Jose Navas Gail Ackermann Greg HumphreyWilliam J. Sandborn Lab Elisabeth Evans John Chang Brigid Boland
Dell/R SystemsBrian KucicJohn Thompson